Rooks Tyler F, Chancey Valeta Carol, Brozoski Frederick T, Salzar Robert S, Pintar Frank A, Yoganandan Narayan
a Injury Biomechanics Division, U.S. Army Aeromedical Research Laboratory (USAARL) , Fort Rucker , Alabama.
b Center for Applied Biomechanics, University of Virginia , Charlottesville , Virginia.
Traffic Inj Prev. 2018;19(sup2):S178-S181. doi: 10.1080/15389588.2018.1532221.
Pelvis injury mechanisms are dependent upon loading direction (frontal, lateral, and vertical). Studies exist on the frontal and lateral modes; however, similar studies in the vertical mode are relatively sparse. Injury risk curves and response corridors are needed to delineate the biomechanical responses. The objective of the study was to derive risk curves for pelvis injuries using postmortem human subjects (PMHSs).
Published data from whole-body PMHSs loaded axially through the pelvis were analyzed. Accelerometers were placed on the pelvis/sacrum and seat. Specimens were loaded along the inferior to superior direction using a horizontal sled or a vertical accelerator device. Specimens were positioned supine in the horizontal sled and seated upright on the vertical accelerator. Pre- and posttest images were obtained and autopsies were completed to document the pathology. Variables used in the development of risk curves included velocity, acceleration, time to peak acceleration, pulse duration of acceleration, and jerk for the seat and sacrum. Survival analysis was used for risk curves. To determine the best predictor of pelvis injury, the Brier Score metric (BSM) was used. The best parametric distribution was determined using the corrected Akaike information criterion (AICc). Injury data points were treated as either uncensored or left/interval censored. Noninjury data points were treated as right censored.
Twenty-four PMHS specimens were identified from 3 published data sets. Fifteen PMHS specimens sustained injuries and 9 remained intact. The BSM ranged from 1.24 to 24.75 and, in general, the BSMs for the seat metric-related scores were greater than the sacrum data. The sacrum acceleration was the optimal metric for predicting pelvis tolerance (lowest BSM). The Weibull distribution had the lowest AICc, with right and left/interval-censored data. This was also true when injury data were treated as exact (uncensored) observations. The 50% probability of injury was associated with 229 G for the uncensored analysis and 139 G for the censored analysis, and the quality indices in both cases were in the "good" range.
Statistical determination of the best injury metric will help improve the accuracy of injury prediction, prioritize instrumentation choice in dummy development, and improve design criteria for crash mitigation. The present study showed that injury risk curves using response data are better biomechanical descriptors of human responses than exposure data. These data are important in automotive safety because complex loading of the pelvis, including submarining, occurs in frontal car crashes.
骨盆损伤机制取决于加载方向（正面、侧面和垂直方向）。关于正面和侧面加载模式已有相关研究；然而，垂直加载模式下的类似研究相对较少。需要损伤风险曲线和响应走廊来描述生物力学响应。本研究的目的是使用尸体人类受试者（PMHS）得出骨盆损伤的风险曲线。
分析了已发表的通过骨盆轴向加载的全身PMHS数据。在骨盆/骶骨和座椅上放置了加速度计。使用水平雪橇或垂直加速装置沿下至上方向对标本进行加载。标本仰卧于水平雪橇上，直立于垂直加速器上。获取测试前后的图像并完成尸检以记录病理情况。用于风险曲线制定的变量包括速度、加速度、峰值加速度时间、加速度脉冲持续时间以及座椅和骶骨的急动度。生存分析用于风险曲线。为确定骨盆损伤的最佳预测指标，使用了布里尔评分指标（BSM）。使用校正的赤池信息准则（AICc）确定最佳参数分布。损伤数据点被视为未删失或左删失/区间删失。非损伤数据点被视为右删失。
从3个已发表的数据集中识别出24个PMHS标本。15个PMHS标本受伤，9个保持完好。BSM范围为1.24至24.75，总体而言，与座椅指标相关评分的BSM大于骶骨数据。骶骨加速度是预测骨盆耐受性的最佳指标（最低BSM）。对于右删失和左删失/区间删失数据，威布尔分布的AICc最低。当将损伤数据视为精确（未删失）观察值时也是如此。对于未删失分析，损伤概率为50%时对应的加速度为229G，对于删失分析为139G，两种情况下的质量指标均处于“良好”范围。
通过统计学方法确定最佳损伤指标将有助于提高损伤预测的准确性，在假人开发中确定仪器选择的优先级，并改进碰撞缓解的设计标准。本研究表明，使用响应数据的损伤风险曲线比暴露数据更能准确描述人体的生物力学响应。这些数据在汽车安全中很重要，因为在正面汽车碰撞中会发生包括下潜在内的复杂骨盆加载情况。
